Wu Chengyue, Hormuth David A, Easley Ty, Pineda Federico, Karczmar Gregory S, Yankeelov Thomas E
University of Texas at Austin, Oden Institute for Computational Engineering and Sciences, Austin, Texas, United States.
MD Anderson Cancer Center, Department of Imaging Physics, Houston, Texas, United States.
J Med Imaging (Bellingham). 2024 Mar;11(2):024002. doi: 10.1117/1.JMI.11.2.024002. Epub 2024 Mar 8.
Validation of quantitative imaging biomarkers is a challenging task, due to the difficulty in measuring the ground truth of the target biological process. A digital phantom-based framework is established to systematically validate the quantitative characterization of tumor-associated vascular morphology and hemodynamics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).
A digital phantom is employed to provide a ground-truth vascular system within which 45 synthetic tumors are simulated. Morphological analysis is performed on high-spatial resolution DCE-MRI data (spatial/temporal resolution = 30 to ) to determine the accuracy of locating the arterial inputs of tumor-associated vessels (TAVs). Hemodynamic analysis is then performed on the combination of high-spatial resolution and high-temporal resolution (spatial/temporal resolution = 60 to to 10 s) DCE-MRI data, determining the accuracy of estimating tumor-associated blood pressure, vascular extraction rate, interstitial pressure, and interstitial flow velocity.
The observed effects of acquisition settings demonstrate that, when optimizing the DCE-MRI protocol for the morphological analysis, increasing the spatial resolution is helpful but not necessary, as the location and arterial input of TAVs can be recovered with high accuracy even with the lowest investigated spatial resolution. When optimizing the DCE-MRI protocol for hemodynamic analysis, increasing the spatial resolution of the images used for vessel segmentation is essential, and the spatial and temporal resolutions of the images used for the kinetic parameter fitting require simultaneous optimization.
An validation framework was generated to systematically quantify the effects of image acquisition settings on the ability to accurately estimate tumor-associated characteristics.
由于难以测量目标生物过程的真实情况,定量成像生物标志物的验证是一项具有挑战性的任务。基于动态对比增强磁共振成像(DCE-MRI),建立了一个基于数字体模的框架,以系统地验证肿瘤相关血管形态和血流动力学的定量特征。
采用数字体模提供一个真实的血管系统,在其中模拟45个合成肿瘤。对高空间分辨率的DCE-MRI数据(空间/时间分辨率 = 30至 )进行形态学分析,以确定定位肿瘤相关血管(TAVs)动脉输入的准确性。然后对高空间分辨率和高时间分辨率(空间/时间分辨率 = 60至 至10秒)的DCE-MRI数据组合进行血流动力学分析,确定估计肿瘤相关血压、血管提取率、间质压力和间质流速的准确性。
采集设置的观察效果表明,在为形态学分析优化DCE-MRI协议时,提高空间分辨率是有帮助的,但不是必需的,因为即使在研究的最低空间分辨率下,TAVs的位置和动脉输入也能高精度恢复。在为血流动力学分析优化DCE-MRI协议时,提高用于血管分割的图像的空间分辨率至关重要,并且用于动力学参数拟合的图像的空间和时间分辨率需要同时优化。
生成了一个验证框架,以系统地量化图像采集设置对准确估计肿瘤相关特征能力的影响。